name: ab-testing-patterns
version: 1.0.0
description: A/B testing methodology for cold email optimization
plugin: instantly
updated: 2026-01-20
A/B Testing Patterns
Testing Fundamentals
One Variable at a Time
CRITICAL: Only change one element per test for clear attribution.
| Test Type |
Variable |
Keep Same |
| Subject Line |
Subject only |
Body, CTA, timing |
| Opening Line |
First sentence |
Subject, rest of body |
| CTA |
Call to action |
Subject, body intro |
| Send Time |
Delivery time |
All copy elements |
Sample Size Requirements
| Confidence Level |
Minimum Sample per Variant |
| 90% |
100 |
| 95% (standard) |
150 |
| 99% |
200 |
Formula:
sample_size = (Z^2 * p * (1-p)) / E^2
Where:
Z = 1.96 for 95% confidence
p = expected conversion rate (use 0.5 if unknown)
E = margin of error (typically 0.05)
Subject Line Testing
Test Categories
| Category |
Control Example |
Variant Example |
| Curiosity vs Specific |
"Quick question" |
"2 min about {{company}}'s pipeline" |
| Personal vs Generic |
"{{first_name}}, saw this" |
"Your team might like this" |
| Question vs Statement |
"Struggling with X?" |
"How we fixed X for [Company]" |
| Short vs Medium |
"Quick win?" |
"{{first_name}}, 2 ideas for {{company}}" |
Best Practices
- Test 2-3 variants maximum - More variants require more sample
- Run for minimum 3 days - Account for daily patterns
- Test during stable periods - Avoid holidays, major events
- Document everything - Record hypothesis, results, learnings
Body Copy Testing
Elements to Test
| Element |
Low-Lift |
High-Lift |
| Opening hook |
Different pain point |
Different approach entirely |
| Social proof |
Different company name |
No social proof |
| Value proposition |
Reframe benefit |
Different benefit |
| CTA |
Soft vs hard ask |
Different action |
Copy Frameworks to Test
PAS vs AIDA:
- PAS: Problem-Agitate-Solution (emotional)
- AIDA: Attention-Interest-Desire-Action (logical)
Test Hypothesis: PAS performs better for pain-point-heavy ICPs, AIDA for solution-seekers.
Timing Tests
Variables to Test
| Variable |
Options to Test |
| Day of week |
Tue vs Thu (typically best) |
| Time of day |
8-10am vs 2-4pm |
| Timezone |
Send in prospect's local time vs batch send |
| Sequence gaps |
2-day vs 3-day follow-up gaps |
Default Schedule (Starting Point)
Optimal Sending Windows:
Primary: Tuesday-Thursday, 9-11am local time
Secondary: Tuesday-Thursday, 2-4pm local time
Avoid: Monday morning, Friday afternoon
Statistical Significance
Quick Significance Check
| Total Sample |
Lift Needed for 95% Confidence |
| 200 (100 per variant) |
15%+ lift |
| 500 (250 per variant) |
10%+ lift |
| 1000 (500 per variant) |
7%+ lift |
Decision Framework
IF lift >= 15% AND sample >= 100/variant:
Declare winner with medium confidence
IF lift >= 10% AND sample >= 250/variant:
Declare winner with high confidence
IF lift < 10% OR sample < 100/variant:
Continue test or call it inconclusive
Implementing A/B Tests in Instantly
Method 1: Split Leads
- Export lead list
- Randomly split into Variant A and Variant B groups
- Create two identical campaigns with one variable different
- Use
move_leads_to_campaign to assign leads
Method 2: Sequential Testing
- Run Control for X days, collect metrics
- Update campaign with Variant (
update_campaign_sequence)
- Run Variant for X days, collect metrics
- Compare (less rigorous, use only if lead volume is limited)
Tracking Results
## A/B Test Log
**Test ID**: {uuid}
**Campaign**: {campaign_name}
**Variable**: {what_was_tested}
**Hypothesis**: {expected_outcome}
**Control**:
- Version: {control_description}
- Sample: {n}
- Open Rate: {x}%
- Reply Rate: {y}%
**Variant**:
- Version: {variant_description}
- Sample: {n}
- Open Rate: {x}%
- Reply Rate: {y}%
**Result**: {Winner|Inconclusive}
**Lift**: {z}%
**Confidence**: {confidence}%
**Learning**: {what_we_learned}
By